{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 44/44 [00:02<00:00, 21.94it/s]\n"
     ]
    }
   ],
   "source": [
    "# Load the feed infos from \n",
    "import os\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "\n",
    "path = \"../../Data/FeedData/full_feed_download\"\n",
    "files = os.listdir(path)\n",
    "\n",
    "def get_feed_data(x):\n",
    "    return {'feed_uri': x['uri'], \n",
    "    'creator_cid': x['cid'], \n",
    "    'feed_createdAt': x['value'].get('createdAt',\"\"),\n",
    "    'feed_description': x['value'].get('description',\"\"),\n",
    "    'feed_displayname': x['value'].get('displayName',\"\")\n",
    "    }\n",
    "\n",
    "all_feeds = []\n",
    "for file in tqdm(files):\n",
    "    with open(f\"{path}/{file}\") as f:\n",
    "        data = json.load(f)\n",
    "    for user in data:\n",
    "        for feed in data[user]:\n",
    "            feed_data = get_feed_data(feed)\n",
    "            feed_data['creator_did'] = user\n",
    "            all_feeds.append(feed_data)\n",
    "\n",
    "scraped_feeds = pd.DataFrame(all_feeds)\n",
    "scraped_feeds = scraped_feeds[~scraped_feeds[\"feed_uri\"].duplicated()].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18357 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18357/18357 [00:33<00:00, 554.18it/s]\n"
     ]
    }
   ],
   "source": [
    "# Load the Feed Likers\n",
    "def get_likers(liker_dict):\n",
    "    results = []\n",
    "    feed_uri = list(liker_dict.keys())[0]\n",
    "    for liker in liker_dict[feed_uri]:\n",
    "        results.append({'feed_uri': feed_uri,\n",
    "                        'liker_did': liker[0],\n",
    "                        'liker_dsplayname': liker[1],\n",
    "                        'liker_description': liker[2],\n",
    "                        'liker_createdAt': liker[3]\n",
    "                         })\n",
    "    return results\n",
    "\n",
    "\n",
    "path = \"../../Data/FeedData/feed_likes\"\n",
    "files = os.listdir(path)\n",
    "\n",
    "with open(f\"{path}/{files[0]}\") as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "scraped_likers = []\n",
    "\n",
    "for file in tqdm(files):\n",
    "    with open(f\"{path}/{file}\") as f:\n",
    "        data = json.load(f)\n",
    "    for feed in data:\n",
    "        scraped_likers.extend(get_likers({feed:data[feed]}))\n",
    "\n",
    "scraped_likers = pd.DataFrame(scraped_likers)\n",
    "scraped_likers = scraped_likers.drop_duplicates(subset = [\"liker_did\",\"feed_uri\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Feeds - Number of Likers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllr}\n",
      "\\toprule\n",
      " & Displayname & Description & Number of Likes \\\\\n",
      "\\midrule\n",
      "2 & For You & Learns what you like... & 16132 \\\\\n",
      "0 & OnlyPosts & Posts from people you follow without rep... & 5137 \\\\\n",
      "5 & Science & The Science Feed. A curated feed from Bl... & 4972 \\\\\n",
      "3 & 🔞 Adult Content & Formerly \"Suggestive\". All (nonviolent) ... & 4180 \\\\\n",
      "4 & Art & Images posted by artists on Bluesky... & 3268 \\\\\n",
      "8 & 🐾 New & Posts by furries across Bluesky. Contain... & 3221 \\\\\n",
      "1 & Mutuals & Posts from users who are following you b... & 3103 \\\\\n",
      "6 & Home+ & Its the Home feed Blue Sky was missing u... & 3050 \\\\\n",
      "9 & 🐾 Art & Posts by furries with #furryart. Contain... & 2938 \\\\\n",
      "7 & Blacksky & Amplifying the voices of any and all Bla... & 2778 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/b/dquell/.local/lib/python3.10/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
     ]
    }
   ],
   "source": [
    "top_feeds = pd.merge(scraped_feeds,scraped_likers.groupby(\"feed_uri\").size().sort_values(ascending = False).head(10).reset_index(), on = \"feed_uri\")\n",
    "top_feeds = top_feeds.loc[:,[\"feed_displayname\",\"feed_description\",0]]\n",
    "top_feeds.columns = [\"Displayname\",\"Description\",\"Number of Likes\"]\n",
    "top_feeds = top_feeds.sort_values(\"Number of Likes\", ascending = False)\n",
    "top_feeds[\"Description\"] = top_feeds[\"Description\"].apply(lambda x: x[0:40]+ \"...\")\n",
    "print(top_feeds.to_latex(index_names=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Likers per Feed Distribution, Number of Feeds per Person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_likers_per_feed = scraped_likers.groupby(\"feed_uri\").size().values\n",
    "num_feeds_per_person = scraped_feeds.groupby('creator_did').size().values\n",
    "num_likes_per_liker = scraped_likers.groupby('liker_did').size().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrr}\n",
      "\\toprule\n",
      " & mean & std & skewness & kurtosis & min & max & ratio & power_law_exponent \\\\\n",
      "\\midrule\n",
      "# \\# Likes per Feed & 2.128 & 5.932 & 203.296 & 60656.913 & 1 & 1799 & 1.183 & 3.413 \\\\\n",
      "# Feeds Created per Person & 2.161 & 13.931 & 122.996 & 16082.634 & 1 & 1828 & 1.182 & 3.350 \\\\\n",
      "# Likers Per Feed & 14.783 & 156.641 & 62.968 & 5800.619 & 1 & 16132 & 0.916 & 1.877 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "def compute_CCDF(data, normalize=True):\n",
    "    x = np.sort(data)[::-1]\n",
    "    counts = x.size - np.searchsorted(x[::-1],x[::-1],side='left') # we need searchsorted to take into account duplicated values\n",
    "\n",
    "    if normalize:\n",
    "        counts = np.array(counts)/ sum(counts)\n",
    "    return x[::-1], counts\n",
    "\n",
    "\n",
    "def find_exponent(degree_sequence, MIN):\n",
    "  degree_sequence = np.array(degree_sequence)\n",
    "  degree_sequence=degree_sequence[np.where(degree_sequence>=MIN)]\n",
    "  return 1+len(degree_sequence)/ sum([np.log(k_i/MIN) for k_i in degree_sequence ])\n",
    "\n",
    "\n",
    "def get_statistics(distribution):\n",
    "    \"\"\"\n",
    "    Returns (dict):\n",
    "        - mean μ, \n",
    "        - standard deviation σ, \n",
    "        - skewness γ, \n",
    "        - kurtosis β, \n",
    "        - minimum m, \n",
    "        - maximum M\n",
    "        - mean over maximum ratio\n",
    "    \"\"\"\n",
    "    μ = np.mean(distribution)\n",
    "    σ = np.std(distribution)\n",
    "    γ = np.mean((distribution - μ) ** 3) / σ ** 3\n",
    "    β = np.mean((distribution - μ) ** 4) / σ ** 4\n",
    "    m = np.min(distribution)\n",
    "    M = np.max(distribution)\n",
    "    ratio = μ/M\n",
    "    #exponent, std_err = calculate_power_law_exponent(distribution)\n",
    "    exponent = find_exponent(distribution, 1)\n",
    "    return {\n",
    "        \"mean\": μ,\n",
    "        \"std\": σ,\n",
    "        \"skewness\": γ,\n",
    "        \"kurtosis\": β,\n",
    "        \"min\": m,\n",
    "        \"max\": M,\n",
    "        \"ratio\": ratio,\n",
    "        \"power_law_exponent\": exponent\n",
    "        #\"power_law_exponent_std_err\": std_err\n",
    "    }\n",
    "\n",
    "\n",
    "results = {}\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "for key, values in [(\"# Likers Per Feed\", num_likers_per_feed), \n",
    "               (\"# Feeds Created per Person\", num_feeds_per_person), \n",
    "               (\"# Number of Likes per Feed\", num_likes_per_liker)]:\n",
    "    results[key] = get_statistics(values)\n",
    "d = pd.DataFrame(results).T\n",
    "d[\"ratio\"] = d[\"ratio\"] * 10**3\n",
    "#d.pop(\"power_law_exponent_std_err\")\n",
    "# Number of digits = 3\n",
    "print(d.sort_values(\"mean\").to_latex(float_format=\"%.3f\").replace(\".000\",\"\").replace(\"Number of\", \"\\#\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['cmex10'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['DejaVu Sans Mono'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
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",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'FreeSans']\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True, sharex=True)\n",
    "axs = axs.flatten()\n",
    "fontsize = 16\n",
    "titlesize = 20\n",
    "markersize = 30\n",
    "labsize = 18\n",
    "grid_bool = False\n",
    "markeralpha = 0.75\n",
    "markercolor = \"#0000ff\"\n",
    "markercolor_2 = \"#800080\"\n",
    "markercolor_3 = \"#006400\"\n",
    "\n",
    "\n",
    "counts = num_likers_per_feed\n",
    "bins = np.logspace(np.log10(min(counts)), np.log10(max(counts)), num=10**3)\n",
    "hist, bin_edges = np.histogram(counts, bins=bins)\n",
    "bin_midpoints =  (bin_edges[:-1] * bin_edges[1:]) ** 0.5\n",
    "axs[0].scatter(\n",
    "    bin_midpoints,hist, s=markersize, alpha=markeralpha, color=\"#800080\"\n",
    ")\n",
    "\n",
    "axs[0].set_xscale(\"log\")\n",
    "axs[0].set_yscale(\"log\")\n",
    "axs[0].set_xlabel(\"Likes Per Feed\", fontsize=titlesize)\n",
    "axs[0].set_ylabel(\"Count\", fontsize=titlesize)\n",
    "\n",
    "\n",
    "counts = num_feeds_per_person\n",
    "bins = np.logspace(np.log10(min(counts)), np.log10(max(counts)), num=10**3)\n",
    "hist, bin_edges = np.histogram(counts, bins=bins)\n",
    "bin_midpoints = (bin_edges[:-1]*bin_edges[1:])**0.5\n",
    "axs[1].scatter(\n",
    "    bin_midpoints,hist, s=markersize, alpha=markeralpha, color=\"#006400\"\n",
    ")\n",
    "\n",
    "axs[1].set_xscale(\"log\")\n",
    "axs[1].set_yscale(\"log\")\n",
    "axs[1].set_xlabel(\"Feeds Created By User\", fontsize=titlesize)\n",
    "\n",
    "counts = num_likes_per_liker\n",
    "bins = np.logspace(np.log10(min(counts)), np.log10(max(counts)), num=10**3)\n",
    "hist, bin_edges = np.histogram(counts, bins=bins)\n",
    "bin_midpoints = (bin_edges[:-1] * bin_edges[1:]) ** 0.5\n",
    "axs[2].scatter(\n",
    "    bin_midpoints,hist, s=markersize, alpha=markeralpha, color=\"#006400\"\n",
    ")\n",
    "\n",
    "axs[2].set_xscale(\"log\")\n",
    "axs[2].set_yscale(\"log\")\n",
    "axs[2].set_xlabel(\"Feeds Liked Per User\", fontsize=titlesize)\n",
    "\n",
    "labels = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\", \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\"]\n",
    "for ax in axs:\n",
    "    min_val = min([ax.get_ylim()[0] for ax in axs])\n",
    "    max_val = max([ax.get_ylim()[1] for ax in axs]) \n",
    "    #max_val = 10**6\n",
    "    ax.set_ylim(min_val, max_val)\n",
    "\n",
    "for idx, ax in enumerate(axs.flat):\n",
    "    ax.text(0.0, 1.025, labels[idx], transform=ax.transAxes, fontsize=28)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../Plots/Feeds_dist.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig(\"../Plots/Feeds_dist.pdf\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
